Smart Cities

The integration of renewable generation sources in cities is a reality. Specifically, photovoltaic technology is the most used (facades, roofs, urban spaces, etc.). The existence of buildings at different altitudes and other urban obstacles can cause shadows in the photovoltaic modules. These shadows will cause the decrease of photovoltaic efficiency. Therefore, the increase in photovoltaic efficiency is essential. This increase in efficiency can be achieved by associating DC-DC converters (DC-DC Optimizers) with photovoltaic modules. This work presents real results of the increase of efficiency of the photovoltaic modules, from the effect of the DC-DC Optimizers. In addition, the work shows simulations of the shadow effect on photovoltaic modules.

Smart city initiatives have emerged to mitigate the negative effects of a very fast growth of urban areas. A number of universities are applying smart city solutions to face similar challenges in their campuses. In this study, we analyze the possibility of using low cost sensors based on detecting wireless signals of light commodity devices to track the movement of the members of the university community. This tracking information will help the university managers to provide the users with smart services. The first insight is that there were not detected barely movements through the campus during late-night/early morning hours (from 0:00H to 6:00H). In turn, the number of human flows sensed in a given direction is similar to the ones in the opposite one. The analysis of the sensed data has shown that the most mobility occurs during the opening and finishing school hours, as expected. Finally, we observed that the sensors are able to detect vehicular mobility.

Nowadays, industrial parks are seen as spaces for the integration of demand and electricity generation. The proximity of the industrial parks to the Smart City, makes possible the employment of advanced techniques for the prediction of the demand and electric generation. This paper presents a complete experiment to choose a model of Short-Term Load Forecasting in industrial parks. The models used are based on artificial intelligence, and different input variables have been tested on all models.

PVCOM project has as objective the development of new solutions for the integration of PV technology in urban applications. Despite traditional glass-glass configuration, the use of composite materials for encapsulation is proposed because its high transparency, low weight and great integration possibilities. Four solutions, a solar table, a solar slate tile, a PV shelter and an electric vehicle roof are proposed. For each proposed application conceptual designs using c-Si and CIGS technologies are done. Conceptual designs are based on legal and end-user’s requirements and involved technologies limitations. More appropriated resin systems and PV system configuration have been also defined. Conceptual designs will be used as reference for the manufacturing of conceptual-proof specimens, testing and the detailed design of final prototypes.

Due to the vast deployment from distributed to large-scale renewable generation, electrical power systems are being equipped more and more with tools improving the controllability of power flows and state monitoring and prospective. In this regard, future power networks are evolving into smart grids. One of the tools for the modernization and decarbonization of power networks is the field of Energy Storage Systems (ESSs). This paper proposes a classification for the many services the ESSs can provide in power systems dominated by renewable-based generation. Three categories of services are defined in terms of the power and energy ratings of the ESS and the main type of beneficiary in each case. For each service, the most suitable type of ESS is identified, exemplary projects are noted and key regulatory issues are highlighted.

The deployment of wireless sensor networks in smart cities for environmental monitoring is a complex issue. One of the main problems is to determine the most appropriate places for these tasks. This paper proposes the use of information from crowdsourcing to identify places of interest from the environmental point of view to deploy the sensor network.

Cities have evolved towards a new paradigm called Smart City (SC), which must evolve towards new intelligent infrastructures, which will integrate new sensors and advanced communications. Energy efficiency is key and fundamental in the SC. The transformation of energy systems due to the increased deployment of renewable energy is occurring mostly in the electricity sector, in which recent PV numbers show an undeniable landmark in renewable energies. Being able to detect, to identify and to quantify the severity of defects that appear within modules is essential to constitute a reliable, efficient and safety system, avoiding energy losses, mismatches and safety issues, especially in case of building integrated systems, as overheated anomalies could generate a fire risk or an electrical hazard. The main objective of this paper is to perform an in-depth on-site study of 17,142 monocrystalline modules to detect every single existing defect manually, classifying them in different groups, studying the variance of the same kind of defect in different modules and the patterns of each group of thermal defects that can be used to develop a software to automatically detect if a module has an anomaly and its classification. Attending the results obtained, all faults detected have been classified in five different thermographic defects modes: hotspot in a cell, bypass circuit overheated, hotspot in the junction box, hotspot in the connection of the busbar to the junction box and whole module overheated, with a percentage of occurrence of 75.35%, 10.79%, 6.93%, 6.84% and 0.09%, respectively.

The paper describes the use of the communications technologies in the electrical network in the last decades and it provides a quick look at the significant role of these technologies in the development of new functionalities. Hence, the great evolution of the requirements of the electrical networks is summarized, from the first stages of the remote automation to a new scenario where Smart Grids and demand response will generate a different relation between utilities and final users. Then, a compilation of the main network architectures and communication protocols used in the electrical networks is outlined. Moreover, an evaluation of the benefits and drawbacks of the communication technologies, when they are applied to the last mile connectivity in electrical grids, is described. The paper concludes with a selection of the most relevant challenges of the electrical networks where the communication technologies may ne determinant as an enabling technology. In summary, the paper shows the parallel evolution of the communication technologies and the electrical grid, as a basic aspect for the development of new functionalities and services for all the agents involved in the power generation-transmission-distribution system.

Residential garbage collection is an important urban issue to address in modern cities, being a key activity that explains a large proportion of budget expenses for local governments. Under the smart cities paradigm, specific solutions can be developed to plan a better garbage collection system, improving the quality of service provided to citizens and reducing costs. This article addresses the problem of selecting locations for community bins in a medium size Argentinian city, that stills uses a door-to-door system. An integer programming model is presented to locate community bins that minimize the installment cost while also maximize the days between two consecutive visit of the collection vehicle. Results demonstrate that the proposed model and the proposed resolution algorithm were able to provide a set of suitable solutions that can be used as a starting point for migrating from the current door-to-door system to a community bins system.

This paper describes the Cloud Computing for Smart Energy Management (CC-SEM) project, a research effort focused on building an integrated platform for smart monitoring, controlling, and planning energy consumption and generation in urban scenarios. The project integrates cutting-edge technologies (Big Data analysis, computational intelligence, Internet of Things, High Performance Computing and Cloud Computing), specific hardware for energy monitoring/controlling built within the project and explores their communication. The proposed platform considers the point of view of both citizens and administrators, providing a set of tools for controlling home devices (for end users), planning/simulating scenarios of energy generation (for energy companies and administrators), and shows some advances in communication infrastructure for transmitting the generated data.

Whether due to economic pressure or environmental concerns, the penetration rate of renewable energies has been increasing over recent years. Uruguay is a leader country in the usage of renewable energies, getting 98% of its electricity from such sources. Its lack of fossil energy resources has historically pushed this country to rely on hydro-energy. Recently, in a scenario where most natural hydro-resources have been deployed, Uruguay has moved to non-conventional renewable energies, to biomass and wind power mostly, although nowadays solar sources are rapidly increasing. As clean and financially stable as they are, non-conventional energies have weaknesses. Unlike thermic and most hydro-sources, wind and solar energies are not controllable, are intermittent and uncertain some hours ahead, complicating the short-term operation and maintenance of electrical systems. This work explores how to use smart-grids capabilities to adjust electricity demand as a natural hedge against novel short-position risks in the Uruguayan electricity market.

This article presents a system that uses computational intelligence to detect pedestrian movement patterns by applying image processing and pattern detection. The system is capable of processing in real time multiple image/video sources and it is based on a pipes and filters architecture that makes it easy to evaluate different computational intelligence techniques. The system counts with two main stages: the first stage extracts the relevant features of images and the second stage is responsible for the detection of patterns. The experimental analysis performed over more than 1450 problem instances covers the two main stages of the system. The system was evaluated using PETS09-S2L1 videos and the results were compared with part of the MOTChallenge benchmark results. Results suggest that the proposed system is competitive, yet simpler, than other similar software methods.

The evolution of the information and communication technologies (ICT) and the need to solve and improve some services in large cities such as environmental monitoring, health, traffic, etc., day by day, new sensors capable of taking parameters of the environment are developed. These sensors must be integrated into larger networks and, in turn, these networks must be integrated into a bigger network so that these sensors together can improve the efficiency and sustainability of cities. These cities equipped with sensors are known as Smart cities. This paper presents an architecture and communication protocol for interconnecting all these sensors and networks. The proposal is group-based architecture able to connect the different infrastructures that provide services to the smart cities. The proposed system is scalable and fault-tolerant. The paper also provides the mathematical model for this interconnection system. Finally, the system is simulated in different topologies to see its operation and performance. The results show that although the size of network increases the amount of generated traffic remains quite stable.

The implementation of the very low geothermal energy is not as extended as the rest of renewable energies. The high initial investment these systems usually require makes them unaffordable for most users. In this regard, this research tries to emphasize the importance of a suitable dimensioning of the whole geothermal plant. With that aim, three different calculation methods have been presented. One of them is based on manual calculations using standard values while the two remaining assumptions consider the use of specific geothermal software. Results reveal that the most suitable method is constituted by the implementation of optimized parameters in the geothermal software. These parameters are obtained from a series of previous analysis and laboratory tests. Applying the most appropriated procedure the initial investment is considerably reduced. Additionally, the electricity consumption of the heat pump is also lower using the mentioned calculation. In this way, the present research demonstrates that and adjusted and proper calculation process can make the geothermal system more attractive for a large number of users.

Managing the waste collection service is a challenge in the fast-growing city context. A key to success in planning the collection is having an accurate prediction of the filling level of the waste containers. In this study we present a solution to the waste generation prediction problem based on recurrent neural networks. Particularly, we introduce a deep neuroevolutionary technique to automatically design a deep network that encapsulates the behavior of all the waste containers in a city. We analyze a real world case study consisting of one year of filling level values of 217 containers located in a city in the south of Spain and compare our results to the state-of-the-art. The results show that the predictions of our approach exceeds all its competitors and that its accuracy is a key enabler for an appropriate waste collection planning.